Relevant bibliographies by topics / Dropout

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Dropout'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "Dropout"

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Wolman, Clara, Robert Bruininks, and Martha L. Thurlow. "Dropouts and Dropout Programs." Remedial and Special Education 10, no. 5 (September 1989): 6–20. http://dx.doi.org/10.1177/074193258901000504.

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Burton, Damon, and Rainer Martens. "Pinned by Their Own Goals: An Exploratory Investigation into Why Kids Drop out of Wrestling." Journal of Sport Psychology 8, no. 3 (September 1986): 183–97. http://dx.doi.org/10.1123/jsp.8.3.183.

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Previous research concludes that athletes drop out of sport because of conflicts of interest, but these findings cannot clarify whether dropouts find other activities more appealing or turn to new activities because sport fails to meet their achievement needs. This investigation assessed dropout motives by testing explanations derived from Nicholls' (1984) motivational model and comparing them with traditional dropout questionnaire responses. Wrestling coaches, participants, participants' parents, dropouts, and dropouts' parents completed a 23-item dropout inventory; and participants and dropouts responded to questionnaire items testing Nicholls' task choice predictions. Dropout inventory responses confirmed previous conflict-of-interest findings. In data testing Nicholls' model, participants demonstrated significantly higher perceived ability, better won-loss records, more functional attributions, and more positive expectancies, and valued wrestling success more than dropouts did. These findings supported predictions that wrestlers change activities when continued participation threatens their perceived ability. Disagreement between the conclusions concerning why young athletes drop out of wrestling drawn from the conflict-of-interest explanation and from Nicholls' perceived ability model are discussed, and suggestions for reducing dropout rates are offered.
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Janosz, Michel, Marc LeBlanc, and Bernard Boulerice. "Consommation de psychotropes et délinquance : de bons prédicteurs de l’abandon scolaire ?" Criminologie 31, no. 1 (September 1, 2005): 87–107. http://dx.doi.org/10.7202/017413ar.

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Although empirical links between deviant behavior and school dropout have been extensively demonstrated, the specific influence of drug use and delinquency on school dropout is still not clear and varies across studies. One reason for this lack of consistency may rests upon the way samples of dropouts have been analysed. Recently, Janosz, Le Blanc, Boulerice and Tremblay (1996) constructed and validated a typology of school dropout highlithing the social and psychological diversity of this population. Using a longitudinal sample of adolescents (N=791), we analyzed the predictive relationships of family rebelliousness, drug use and delinquency on school dropout. The results showed an important variability in the predictive relationships according to the type of dropouts. The necessity of considering the psychosocial heterogeneity of dropouts when conducting such studies is discussed.
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Pusztai, Gabriella, Hajnalka Fényes, Fruzsina Szigeti, and Katalin Pallay. "Dropped-out Students and the Decision to Drop-out in Hungary." Central European Journal of Educational Research 1, no. 1 (October 1, 2019): 31–40. http://dx.doi.org/10.37441/cejer/2019/1/1/3341.

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The interpretation of the phenomenon of student dropout, which represents a waste of a relatively large proportion of human and material capital in the social, individual and institutional domain, is impossible without examining dropout students. In this study, we analysed the DEPART 2018 database, which contains data from 605 Hungarian dropout students. We tried to identify higher education dropout scenarios and pointed out that higher educational dropout is a complex phenomenon. Based on the students' reports on their interpretation and evaluation of their dropouts, four student clusters were created. We compared the groups with their socio-cultural background and their decision to drop out from higher education, and their assessment of that decision. The most important result of the study is that it identifies a new group in addition to the international dropout types, and provides a detailed picture that calls our attention to the diversity of dropout groups, thereby moving beyond the over-generalised image of the dropout student.
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Huang, Li-xun, and Yong Fang. "Convergence Analysis of Wireless Remote Iterative Learning Control Systems with Dropout Compensation." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/609284.

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The wireless remote iterative learning control (ILC) system with random data dropouts is considered. The data dropout is viewed as a binary switching sequence which obeys the Bernoulli distribution. In order to eliminate the effect of data dropouts on the convergence property of output error, the signal at the same time with the lost one but in the last iteration is used to compensate the data dropout at the actuator. With the dropout compensation, the convergence property of output error is analyzed by studying the element values of system transition matrix. Finally, some simulation results are given to illustrate the validity of the proposed method.
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Hammack, Floyd Morgan. "Large School Systems' Dropout Reports: An Analysis of Definitions, Procedures, and Findings." Teachers College Record: The Voice of Scholarship in Education 87, no. 3 (March 1986): 324–41. http://dx.doi.org/10.1177/016146818608700303.

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One basic problem for both researchers and policymakers is obtaining accurate information about dropouts. In this article, Floyd Hammack examines school district reports on the dropout problem in Boston, Los Angeles, Miami, New York City, San Diego, and Chicago. Citing the great diversity in the processes for the classification of students as dropouts, he raises important concerns about the comparability of dropout rates between districts.
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Andrea King-Dominguez, Andrea, Luis Amestica-Rivas, Victor Ramirez Gonzalez, and Francisco Ganga Contreras. "Student dropout, the economic cost for Chilean universities." Universidad Ciencia y Tecnología 27, no. 118 (February 27, 2023): 17–28. http://dx.doi.org/10.47460/uct.v27i118.683.

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Student dropout continues to be a significant challenge in higher education. Studies have shown that it has economic impacts on institutions. However, the cost associated with student dropout needs to be better understood. This research focuses on first-year dropouts in 51 Chilean universities, intending to analyze the costs generated by student dropouts in all their careers, allowing to project in an average horizon the resources that would no longer be received as a result of dropouts. It is descriptive research with a documentary analysis technique. Among the most outstanding results, it is shown that the institutions would lose around USD 1,169,853,410 and that private universities are the ones that register a higher cost associated with first-year university dropouts. On the other hand, it is statistically proven that for those universities that register a higher number of enrollments, the cost associated with dropout will be higher.
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Nurmalitasari, Zalizah Awang Long, and Mohammad Faizuddin Mohd Noor. "Factors Influencing Dropout Students in Higher Education." Education Research International 2023 (February 8, 2023): 1–13. http://dx.doi.org/10.1155/2023/7704142.

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Dropout students are a severe problem in higher education (HE) in many countries. Student dropout has a tremendous negative impact not only on individuals but also on universities and socioeconomic. Consequently, preventing educational dropouts is a considerable challenge for HE’s institutions. Therefore, knowing the factors influencing student dropout is an essential first step in preventing students from dropping out. This study uses a mix of qualitative and quantitative approaches. To determine what variables affect student dropout, we use a qualitative approach, after which the variables found will be validated by the public and stakeholders using a quantitative approach. Then, the next step is to classify variables using a quantitative approach. This study observes dropout students at private universities in Central Java, Indonesia. The findings reveal that personal economic factors, academic satisfaction, academic performance, and family economics are the most influential. The results of this paper are significant for universities in Indonesia, especially Central Java, to overcome the problem of student dropouts, so that they are more precise in making decisions. In addition, the results of this study are also helpful for further research as a basis for predicting students dropping out of university.
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Radovanovic, Sandro, Boris Delibasic, and Milija Suknovic. "Predicting dropout in online learning environments." Computer Science and Information Systems, no. 00 (2020): 53. http://dx.doi.org/10.2298/csis200920053r.

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Online learning environments became popular in recent years. Due to high attrition rates, the problem of student dropouts became of immense importance for course designers, and course makers. In this paper, we utilized lasso and ridge logistic regression to create a prediction model for dropout on the Open University database. We investigated how early dropout can be predicted, and why dropouts occur. To answer the first question, we created models for eight different time frames, ranging from the beginning of the course to the mid-term. There are two results based on two definitions of dropout. Results show that at the beginning AUC of the prediction model is 0.549 and 0.661 and rises to 0.681 and 0.869 at mid-term. By analyzing logistic regression coefficients, we showed that at the beginning of the course demographic features of the student and course description features are the most important variables for dropout prediction, while later student activity gains more importance.
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Hernández-Rodríguez, Juan-Carlos, Cristina García-Muñoz, Juan Ortiz-Álvarez, Francesc Saigí-Rubió, Julián Conejo-Mir, and Jose-Juan Pereyra-Rodriguez. "Dropout Rate in Digital Health Interventions for the Prevention of Skin Cancer: Systematic Review, Meta-analysis, and Metaregression." Journal of Medical Internet Research 24, no. 12 (December 9, 2022): e42397. http://dx.doi.org/10.2196/42397.

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Background Digital strategies are innovative approaches to the prevention of skin cancer, but the attrition following this kind of intervention needs to be analyzed. Objective The aim of this paper is to assess the dropouts from studies focused on digital strategies for the prevention of skin cancer. Methods We conducted this systematic review with meta-analyses and metaregression according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statements. Search terms for skin cancer, digital strategies, and prevention were combined to search PubMed, Scopus, Web of Science, CINAHL, and Cochrane Library from inception until July 2022. Randomized clinical trials that reported dropouts of participants and compared digital strategies with other interventions to prevent skin cancer in healthy or disease-free participants were included. Two independent reviewers extracted data for analysis. The Revised Cochrane Collaboration Bias tool was employed. We calculated the pooled dropout rate of participants through a meta-analysis of proportions and examined whether dropout was more or less frequent in digital interventions against comparators via an odds ratio (OR) meta-analysis. Data were pooled using a random-effects model. Subgroup meta-analyses were conducted in a meta-analysis of proportions and OR meta-analysis to assess the dropout events when data were sorted by digital interventions or control comparator. A univariate metaregression based on a random-effects model assessed possible moderators of dropout. Participants’ dropout rates as pooled proportions were calculated for all groups combined, and the digital and comparator groups separately. OR>1 indicated higher dropouts for digital-based interventions. Metaregressions were performed for age, sex, length of intervention, and sample size. Results A total of 17 studies were included. The overall pooled dropout rate was 9.5% (95% CI 5.0-17.5). The subgroup meta-analysis of proportions revealed a dropout rate of 11.6% for digital strategies (95% CI 6.8-19.0) and 10.0% for comparators (95% CI 5.5-17.7). A trend of higher dropout rates for digital strategies was observed in the overall (OR 1.16, 95% CI 0.98-1.36) and subgroup OR meta-analysis, but no significant differences were found between the groups. None of the covariates moderated the effect size in the univariate metaregression. Conclusions Digital strategies had a higher dropout rate compared to other prevention interventions, but the difference was not significant. Standardization is needed regarding reporting the number of and reasons for dropouts. Trial Registration International Prospective Register of Systematic Reviews (PROSPERO) CRD42022329669; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=329669
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Dissertations / Theses on the topic "Dropout"

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Russell, Alecia Marie. "An Effective Dropout Prevention Program for Urban Students." Ashland University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ashland1260899676.

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Owens, Mario Antonio. "Variables that impact high school dropout." Diss., Mississippi State : Mississippi State University, 2009. http://library.msstate.edu/etd/show.asp?etd=etd-03312009-151116.

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Poza-Juncal, Inés Victoria. "Predicting dropout among inner-city Latino youth using psychological indices /." Digital version accessible at:, 1999. http://wwwlib.umi.com/cr/utexas/main.

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Garcia, Daniel. "High School Dropout: A Study of Parental Perceptions of Dropout and Prevention Strategies." Honors in the Major Thesis, University of Central Florida, 2013. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/946.

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Dropping out from high school is becoming a growing trend that necessitates new and more powerful intervention strategies to be utilized. Large numbers of adolescents between the ages of 16- 24 have been shown to receive no high school diploma or equivalent. The effects of dropping out are far reaching, with many students who dropout facing depression and alienation that may lead to other negative consequences. This research focuses on the need for preventative action against the issue of dropout. One hypothesis to be tested is whether or not current dropout prevention techniques can be applied to students who are not at risk for dropping out. The researcher theorizes that if all students are exposed to these strategies then perhaps the overall school environment will yield less students who are at risk to begin with.
B.S.W.
Bachelors
Health and Public Affairs
Social Work
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Huffman, Karen L. "Dropouts and dropout prevention programs how West Virginia high school principals view their roles /." Morgantown, W. Va. : [West Virginia University Libraries], 1999. http://etd.wvu.edu/templates/showETD.cfm?recnum=631.

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Thesis (Ed. D.)--West Virginia University, 1999.
Title from document title page. Document formatted into pages; contains xii, 153 p. : ill. Vita. Includes abstract. Includes bibliographical references (p. 124-137).
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Clement, Priscillia. "High school dropout : systemic implications and the GED as a model of redress for dropouts /." View abstract, 2001. http://library.ccsu.edu/ccsu%5Ftheses/showit.php3?id=1639.

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Thesis (M.S.)--Central Connecticut State University, 2001.
Thesis advisor: Jane Fried. " ... in partial fulfillment of the requirements for the degree of Master of Science in School Counseling." Includes bibliographical references (leaves 75-80). Also available via the World Wide Web.
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Pettersson, Emma, and Evelina Bernting. "Jag hoppade av skolan. Och? I'm a Dropout. So what?" Thesis, Malmö högskola, Lärarutbildningen (LUT), 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-31456.

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Syftet med vår studie har varit att ta reda på varför vissa ungdomar gör avbrott från singymnasieutbildning. Vi har använt oss av en kvalitativ metod och genomfört totalt åttasemistruktuerade intervjuer. Våra respondenter är två dropoutungdomar, som vi intervjuat för att få en inblick i och djupare förståelse för individuella och strukturella orsaker som ligger bakom beslutet att hoppa av skolan. De övriga sex respondenterna är samtliga studie- ochyrkesvägledare, varav fyra är verksamma på kommunala och fristående gymnasieskolor, eninom grundskolan och en arbetar inom det kommunala uppföljningsansvaret. Intervjuernamed dessa professionella personer har vi gjort i syfte att undersöka vilka subjektiva och objektiva förklaringar de har till att elever slutar skolan i förtid och hur de arbetar med dropoutsen. Med denna undersökning har vi kommit fram till att de bakomliggande orsakerna till att vissa elever hoppar av skolan alltid är personliga, men att bakgrunden utgörs av enblandning av individuella och strukturella faktorer. Vårt resultat av intervjuerna har vi tolkat och analyserat med hjälp av Becknes teorier om objektiva och subjektiva förklaringar till studieavbrott. Vi har även utgått från Becknes teori om tidiga och sena avbrytare och delvis kunnat kategorisera våra två dropoutrespondenter enligt denna.
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King, Teresa C. "Examining the Relationship Between Persistence in Attendance in an Afterschool Program and an Early Warning Index for Dropout." Thesis, University of North Texas, 2014. https://digital.library.unt.edu/ark:/67531/metadc500218/.

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School districts constantly struggle to find solutions to address the high school dropout problem. Literature supports the need to identify and intervene with these students earlier and in more systemic ways. The purpose of this study was to conduct a longitudinal examination of the relationship between sustained afterschool participation and the host district’s early warning index (EWI) associated with school dropout. Data included 65,341 students participating in an urban school district’s after school program from school years 2000-2001 through 2011-2012. The district serves more than 80,000 students annually. Data represented students in Pre-Kindergarten through Grade 12, and length of participation ranged from 1 through 12 years. Results indicated that student risk increased over time and that persistent participation in afterschool programming had a significant relationship with student individual growth trajectories. Slower growth rates, as evidenced through successive models, supported students being positively impacted by program participation. Additionally, participation was more meaningful if students persisted, as noted in the lower EWI rates, as compared to students who attended less consistently.
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Maswahu, Israel Lubinda. "Grade 10 dropout predisposition and resilience in one rural and one urban secondary school in the Kizito Cluster of the Caprivi Education Region in Namibia." Thesis, University of the Western Cape, 2012. http://hdl.handle.net/11394/4494.

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Magister Educationis - MEd
The study investigated school dropout predisposition and resilience at Grade 10 level in one rural and one urban secondary school in the Kizito Cluster of the Caprivi Education Region in Namibia. Part of the study was by means of a literature review of studies on school dropout in different parts of the world. The review also related to theoretical explanations of school dropout predisposition and resilience, together with preceding studies about the two phenomena. The qualitative research approach was used to obtain information about participants' experiences, beliefs, and perceptions about the school dropout and resilience phenomena. The study was framed by the interpretive research paradigm. The case study method, along with semi-structured, one-on-one and focus group interviews and document analysis were used to gather data. In addition, the study was also framed by grounded theory for the purposes of identifying alignments between the study’s findings and existing theories whilst also enabling the identification of theoretical gaps regarding some the study’s findings which subsequent studies should address. In addition, grounded theory also helped to present a theoretical discussion of the two studied phenomena. Factors found to be associated with school dropout predisposition in the rural school included over-age and alcohol and drug abuse (in boys), long walking distances to school and teenage pregnancy (in girls), poor academic performance, curriculum, poverty, peer pressure and lack of parental involvement and support in the education of children( in both sexes). Factors that were found to be associated with school dropout resilience at the school included availability of role models, good academic performance, parental involvement and support in education and the teaching of the Life Skills subject. Factors found to be associated with school dropout predisposition in the urban school included curriculum, poor academic performance, over-age, poverty and peer pressure (in both sexes), alcohol and drug abuse, bullying and lack of hostel accommodation (in boys), teenage pregnancy and early marriages or involvement in sexual activities (in girls). Factors that were found to be associated with school dropout resilience at the school included good academic performance, learning from siblings and friends’ dropout experiences, self-determination and self-motivation, and valuing education and motivation from parents and relatives, counselling, praising learners and inviting renowned individuals to speak to children. Factors which were found to be linked to existing dropout predisposition theories were curriculum, poor academic performance and poverty, whilst teenage pregnancy was found not to be linked to existing theories. A factor which was found to be linked to existing dropout resilience theories was good academic performance whilst no factor was found not to be linked to existing theories. Recommendations in this study include calls for exploration by further studies in generating new theories to better explain findings in this study. The recommendations to schools and the Regional Education Directorate to help in enhancing dropout resilience include counselling and academic performance enhancing strategies.
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Dodd, Ann. "High stakes testing effects dropout rates." Huntington, WV : [Marshall University Libraries], 2007. http://www.marshall.edu/etd/descript.asp?ref=813.

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Books on the topic "Dropout"

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J, Andren Kristina, ed. Dropout prevention. New York: The Guilford Press, 2014.

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Dynarski, Mark. Dropout prevention. Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Dept. of Education, 2008.

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translator, Cook Caleb D., and Gancio Rochelle, eds. Gabriel dropout. New York: Yen Press, 2018.

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translator, Cook Caleb D., and Gancio Rochelle, eds. Gabriel dropout. New York, NY: Yen Press, 2018.

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Copyright Paperback Collection (Library of Congress), ed. Dropout blues. New York, N.Y: Crosswinds, 1988.

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Nichols, Clarence E. Dropout prediction and prevention. Brandon, VT: Clinical Psychology Pub. Co., 1990.

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Vampire school dropout? New York: Scholastic, 2012.

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Vernick, Audrey. First grade dropout. Boston: Houghton Mifflin Harcourt Publishing Company, 2015.

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Badger, Meredith. Fairy school dropout. New York, NY: Scholastic, 2010.

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Lee Lozano: Dropout piece. London: Afterall Books, 2014.

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Book chapters on the topic "Dropout"

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Weik, Martin H. "dropout." In Computer Science and Communications Dictionary, 463. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_5624.

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Reschly, Amy, and Karen Sandberg Patton. "School Dropout." In Encyclopedia of Quality of Life and Well-Being Research, 5692–95. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-0753-5_2598.

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Reschly, Amy L., and Christopher A. Pinzone. "School Dropout." In Encyclopedia of Quality of Life and Well-Being Research, 1–4. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-69909-7_2598-2.

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Stewart, Latoya S., Andy V. Pham, and John S. Carlson. "School Dropout." In Encyclopedia of Child Behavior and Development, 1293–94. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-79061-9_2501.

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Matvya, Jennifer, Nancy Lever, and Jennifer Pitchford. "Student Dropout." In Encyclopedia of Cross-Cultural School Psychology, 936–39. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-0-387-71799-9_404.

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Sewell, Trevor E., Joseph P. DuCette, and Erin McNamara Horvat. "School dropout." In Encyclopedia of psychology, Vol. 7., 170–73. Washington: American Psychological Association, 2000. http://dx.doi.org/10.1037/10522-069.

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Schmalfuß, Marcus. "Theoretische Einführung in das Forschungsfeld." In Absentismus und Dropout, 5–142. Wiesbaden: Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-37037-4_2.

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Schmalfuß, Marcus. "Empirischer Zugang zum Forschungsfeld." In Absentismus und Dropout, 143–59. Wiesbaden: Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-37037-4_3.

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Schmalfuß, Marcus. "Einleitung." In Absentismus und Dropout, 1–3. Wiesbaden: Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-37037-4_1.

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Schmalfuß, Marcus. "Ergebnisse der Untersuchung." In Absentismus und Dropout, 161–264. Wiesbaden: Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-37037-4_4.

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Conference papers on the topic "Dropout"

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Morerio, Pietro, Jacopo Cavazza, Riccardo Volpi, Rene Vidal, and Vittorio Murino. "Curriculum Dropout." In 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, 2017. http://dx.doi.org/10.1109/iccv.2017.383.

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Hanumolu, Pavan Kumar. "Low dropout regulators." In 2015 IEEE Custom Integrated Circuits Conference - CICC 2015. IEEE, 2015. http://dx.doi.org/10.1109/cicc.2015.7338435.

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Chen, Yunfan, and Ming Zhang. "MOOC student dropout." In the ACM Turing 50th Celebration Conference - China. New York, New York, USA: ACM Press, 2017. http://dx.doi.org/10.1145/3063955.3063959.

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Whitehill, Jacob, Kiran Mohan, Daniel Seaton, Yigal Rosen, and Dustin Tingley. "MOOC Dropout Prediction." In L@S 2017: Fourth (2017) ACM Conference on Learning @ Scale. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3051457.3053974.

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Nagrecha, Saurabh, John Z. Dillon, and Nitesh V. Chawla. "MOOC Dropout Prediction." In the 26th International Conference. New York, New York, USA: ACM Press, 2017. http://dx.doi.org/10.1145/3041021.3054162.

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Sánchez-Nítola, Mónica Natalí, Diana Grijalba, Manuel Embus, Andrés Suarez, and Juan Esteban Guevara Montoya. "Dropout and Engineering undergraduate programs at the Universidad Nacional de Colombia between 2012-2019." In Sixth International Conference on Higher Education Advances. Valencia: Universitat Politècnica de València, 2020. http://dx.doi.org/10.4995/head20.2020.11270.

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Dropout is a topic of significant concern to Higher Education Institutions, especially in Colombia it is considered one of the most important indicators to define higher education quality. Nevertheless, in Colombia, and particularly at the Universidad Nacional de Colombia, it has been generally assessed only academic dropout. This decision has prevented understanding other types of dropouts that also occur in the university. The research group performed Multiple Correspondence Analysis to assess the relationship between dropout types (academic, dropout on request, and expiry of time limits) and variables such as sex, socioeconomic stratum, type of admission, and semester of dropout. In the results, we found a disparity between types of dropout concerning the type of university admission, sex, socio-economic stratum, and the semester in which dropout occurs. Women are related to the dropout request while men are associated with the academic dropout. Additionally, the academic dropout occurs in the first 4 semesters, and the expiry of time limits occurs in the last ones.
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da Costa, Oberdan Santos, and Luís Borges Gouveia. "Dropout in distance learning." In EATIS '18: Euro American Conference on Telematics and Information Systems. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3293614.3293648.

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Bin Yu and A. K. Jain. "A form dropout system." In Proceedings of 13th International Conference on Pattern Recognition. IEEE, 1996. http://dx.doi.org/10.1109/icpr.1996.547036.

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Ma, Limin, Xingyuan Chen, Bin Wang, and Peng Jin. "Gradient-based Dynamic Dropout." In 2021 17th International Conference on Computational Intelligence and Security (CIS). IEEE, 2021. http://dx.doi.org/10.1109/cis54983.2021.00037.

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ByungSoo Ko, Han-Gyu Kim, Kyo-Joong Oh, and Ho-Jin Choi. "Controlled dropout: A different approach to using dropout on deep neural network." In 2017 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, 2017. http://dx.doi.org/10.1109/bigcomp.2017.7881693.

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Reports on the topic "Dropout"

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Guryan, Jonathan. Desegregation and Black Dropout Rates. Cambridge, MA: National Bureau of Economic Research, June 2001. http://dx.doi.org/10.3386/w8345.

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Chatterji, Pinka, and Jeff DeSimone. Adolescent Drinking and High School Dropout. Cambridge, MA: National Bureau of Economic Research, May 2005. http://dx.doi.org/10.3386/w11337.

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Fennell, Joseph F., James L. Roeder, H. Spence, H. Singer, and A. Korth. CRRES Observations of Particle Flux Dropout Events. Fort Belvoir, VA: Defense Technical Information Center, November 1994. http://dx.doi.org/10.21236/ada296474.

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Miller, Michelle Claire. Testing the Robustness of Dropout Under Irradiation. Office of Scientific and Technical Information (OSTI), July 2019. http://dx.doi.org/10.2172/1545744.

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Fennell, J., J. Roeder, H. Spence, H. Singer, and A. Korth. CRRES Observations of Particle Flux Dropout Events. Fort Belvoir, VA: Defense Technical Information Center, February 1998. http://dx.doi.org/10.21236/ada341651.

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Tofaris, Elizabeth, and Catherine Jere. Reducing School Dropout Rates in Malawi and Lesotho. The Impact Initiative / Institute of Development Studies, November 2017. http://dx.doi.org/10.35648/20.500.12413/11781/ii289.

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Heller, Sara, Harold Pollack, Roseanna Ander, and Jens Ludwig. Preventing Youth Violence and Dropout: A Randomized Field Experiment. Cambridge, MA: National Bureau of Economic Research, May 2013. http://dx.doi.org/10.3386/w19014.

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Kaffenberger, Michelle, Danielle Sobol, and Deborah Spindelman. The Role of Low Learning in Driving Dropout: A Longitudinal Mixed Methods Study in Four Countries. Research on Improving Systems of Education (RISE), April 2021. http://dx.doi.org/10.35489/bsg-rise-wp_2021/070.

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Using unique longitudinal quantitative and qualitative data, we examine the role that low learning plays in driving dropout in Ethiopia, India, Peru, and Vietnam. Regression analysis using IRT-linked test scores and data on schooling attainment and dropout shows a strong, significant association with one standard deviation higher test scores associated with 50 percent lower odds of dropping out between the ages of 8 and 12, and a similar association between the ages of 12 and 15. Qualitative analysis indicates a direct relationship between low learning and dropout, with children and parents choosing to discontinue school when they realize how little is being learned. Qualitative findings also show that low learning interacts with and exacerbates more proximate causes of dropout, with low learning often contributing to choices of early marriage (for girls) and of leaving school to work (for both genders), with families making practical decisions about which options will best provide for children in the long run. Finally, learning, work, and poverty often interact, as the need to work to help provide for the household reduces the opportunities to learn, and low learning tilts the opportunity cost of time in favor of working. These findings suggest that low learning may play a larger role in dropout decisions, by underlying and interacting with other causes, than has been typically recognized.
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McCaffrey, Daniel, Rosalie Liccardo Pacula, Bing Han, and Phyllis Ellickson. Marijuana Use and High School Dropout: The Influence of Unobservables. Cambridge, MA: National Bureau of Economic Research, June 2008. http://dx.doi.org/10.3386/w14102.

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Sealza, Lita. Factors affecting the family planning program drop-out rate in Bukidnon, Philippines. Population Council, 1994. http://dx.doi.org/10.31899/rh1994.1002.

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This study has involved interviews with a randomly selected sample of 389 family planning (FP) acceptors from 20 barangays in Bukidnon province, Philippines. Respondents were currently married women who had either continued to use family planning (FP) since their initial acceptance (current users) or who had stopped using FP altogether (dropouts). Findings from the present study are expected to shed light on several aspects of the dropout problem, namely: How extensive is this problem and how accurate are the DOH records in this regard? How do FP acceptors view their local FP clinic and the services offered? What reasons are given by the dropouts for their decision to stop using FP? What are the factors associated with dropping out? As noted in this report, the study was made possible through support provided by the Population Council under its Family Planning Operations Research and Training (FPORT) Program.
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